Agent from Scratch to Mastery: Comprehensive Architecture & Hands-on Medical Paper Agent Guide

I recently bought a book for my kid called Robotics for Babies.

It starts with making a circle: drawing with a pencil, cutting with scissors; then using a circle cutter for a single-press circle; later introducing machines, sensors, cameras, conveyor belts, and robotic arms, ultimately assembling an autonomous circle-producing robot.

Once LLMs learned to answer questions, people realized that answering alone wasn’t enough. An agent needs real-time data, filesystem access, error feedback loops, and self-correction mechanisms. Products like Claude Code, Codex, and desktop workbenches are the direct outcome of solving these concrete physical bottlenecks.

01. LLMs vs Agents: What's the True Distinction?

LLMs generate answers; Agents get the actual work done.

Feed meeting minutes into an LLM, and you get an action item list in the chat box. Give that same task to an Agent, and it locates the file, parses content, creates a spreadsheet, writes back to the directory, and verifies if any assignees or deadlines were missed.